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关键词:
A Statistical Approach to Neural Networks for Pattern Recognition
书目信息
ISBN:
9780471741084(13位)
中图分类号:
O1
杜威分类号:
中文译名:
用于模式识别的神经网络统计学方法
作者:
Dunne
编者:
语种:
English
出版信息
出版社:
John Wiley & Sons
出版地:
出版年:
2007
版本:
版本类型:
原版
丛书题名:
卷期:
文献信息
关键词:
Applied Probability and Statistics
前言:
摘要:
内容简介:
This book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorough answers. The following are a few questions that are considered in this book and are explored: how robust is the model to outliers, could the model be made more robust, which points will have a high leverage, what are good starting values for the fitting algorithm, etc. Discussions include the use of MLP models with spatial data as well as the influence and sensitivity curves of the MLP. The question of why the MLP is a (fairly) robust model is answered and modifications to make it very robust are considered.
目次:
Notation and Code Examples. Acknowledgments. 1. Introduction. 2. The Multi-Layer Perception Model. 3. Linear Discriminant Analysis. 4. Activation and Penalty Functions. 5. Model Fitting and Evaluation. 6. The Task-Based MLP. 7. Incorporating Spatial Information into an MLP Classifier. 8. Influence Curve for the Multi-Layer Perceptron Classifier. 9. The Sensitivity Curves of the MLP Classifier. 10. A Robust Fitting Procedure for MLP Models. 11. Smoothed Weights. 12. Translation Invariance. 13. Fixed-slope Training. Appendix A. Function Minimization. Appendix B. Maximum Values of the Influence Curve. Topic Index.
附录:
全文链接:
读者对象:
Engineers, scientists, statisticians, and graduate students.
实体信息
页码:
280
装帧:
Cloth
尺寸:
其它形态细节:
其它信息
原价:
USD
89.9500
原版ISBN:
其它ISBN:
图书特色:
书评:
扩展信息
Isbn:
0471741086
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